import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_ta import TaTokenizer
else:
	_UpperCAmelCase          =						None


_UpperCAmelCase          =						logging.get_logger(__name__)

_UpperCAmelCase          =						{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase          =						{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_UpperCAmelCase          =						{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   VOCAB_FILES_NAMES
				lowerCamelCase_        =   PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_        =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_        =   ['''input_ids''', '''attention_mask''']
				lowerCamelCase_        =   TaTokenizer

				lowerCamelCase_        =   []
				def __init__(		self						,      lowercase=None						,      lowercase=None						,      lowercase="</s>"						,      lowercase="<unk>"						,      lowercase="<pad>"						,      lowercase=1_0_0						,      lowercase=None						,      **lowercase						,      ):



						"""simple docstring"""





						if extra_ids > 0 and additional_special_tokens is None:
								A_						:							Optional[int]      = [F'''<extra_id_{i}>''' for i in range(lowercase   )]
						elif extra_ids > 0 and additional_special_tokens is not None:
								# Check that we have the right number of extra special tokens
								A_						:							Optional[Any]      = len(set(filter(lambda lowercase   : bool('extra_id_' in str(lowercase   )   )						,      lowercase   )   )   )
								if extra_tokens != extra_ids:
										raise ValueError(
										    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
										    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
										    ' tokens'   )

						super().__init__(
						    lowercase						,      tokenizer_file=lowercase						,      eos_token=lowercase						,      unk_token=lowercase						,      pad_token=lowercase						,      extra_ids=lowercase						,      additional_special_tokens=lowercase						,      **lowercase						,      )

						A_						:							Union[str, Any]      = vocab_file
						A_						:							Optional[int]      = False if not self.vocab_file else True
						A_						:							Tuple      = extra_ids
				@staticmethod
				def     lowerCAmelCase_  (		lowercase						,      lowercase						,      lowercase   ):



						"""simple docstring"""





						if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
								A_						:							Optional[Any]      = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
								if init_max_model_length is not None and init_max_model_length != max_model_length:
										return init_max_model_length
								elif init_max_model_length is None:
										warnings.warn(
										    'This tokenizer was incorrectly instantiated with a model max length of'
										    F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
										    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
										    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
										    F''' {pretrained_model_name_or_path} automatically truncating your input to'''
										    F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
										    F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
										    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
										    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'						,      lowercase						,      )

						return max_model_length
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase = None   ):



						"""simple docstring"""





						if not self.can_save_slow_tokenizer:
								raise ValueError(
								    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
								    'tokenizer.'   )

						if not os.path.isdir(lowercase   ):
								logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
								return
						A_						:							Optional[Any]      = os.path.join(
						    lowercase						,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

						if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowercase   ):
								copyfile(self.vocab_file						,      lowercase   )
								logger.info(F'''Copy vocab file to {out_vocab_file}'''   )

						return (out_vocab_file,)
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase = None   ):



						"""simple docstring"""





						A_						:							Union[str, Any]      = token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
								return self.prefix_tokens + token_ids_a
						else:
								A_						:							Optional[Any]      = token_ids_a + [self.eos_token_id]
								return self.prefix_tokens + token_ids_a + token_ids_a
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase = None   ):



						"""simple docstring"""





						A_						:							List[str]      = [self.eos_token_id]

						if token_ids_a is None:
								return len(token_ids_a + eos   ) * [0]
						return len(token_ids_a + eos + token_ids_a + eos   ) * [0]
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return list(
						    set(filter(lambda lowercase   : bool(re.search(r'<extra_id_\d+>'						,      lowercase   )   ) is not None						,      self.additional_special_tokens   )   )   )







				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return [self.convert_tokens_to_ids(lowercase   ) for token in self.get_sentinel_tokens()]







from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


_UpperCAmelCase          =						logging.get_logger(__name__)





class UpperCAmelCase		:


				'''simple docstring'''






				lowerCamelCase_        =   42
				lowerCamelCase_        =   None
				@staticmethod
				def     lowerCAmelCase_  (		):



						"""simple docstring"""





						raise NotImplementedError
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						raise NotImplementedError
				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						raise NotImplementedError
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						if not self.is_available():
								raise RuntimeError(
								    F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''   )







				@classmethod
				def     lowerCAmelCase_  (		cls   ):



						"""simple docstring"""





						return F'''`pip install {cls.pip_package or cls.name}`'''





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''optuna'''
				@staticmethod
				def     lowerCAmelCase_  (		):



						"""simple docstring"""





						return is_optuna_available()
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						return run_hp_search_optuna(lowercase						,      lowercase						,      lowercase						,      **lowercase   )







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						return default_hp_space_optuna(lowercase   )





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''ray'''
				lowerCamelCase_        =   '''\'ray[tune]\''''
				@staticmethod
				def     lowerCAmelCase_  (		):



						"""simple docstring"""





						return is_ray_available()
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						return run_hp_search_ray(lowercase						,      lowercase						,      lowercase						,      **lowercase   )







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						return default_hp_space_ray(lowercase   )





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''sigopt'''
				@staticmethod
				def     lowerCAmelCase_  (		):



						"""simple docstring"""





						return is_sigopt_available()
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						return run_hp_search_sigopt(lowercase						,      lowercase						,      lowercase						,      **lowercase   )







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						return default_hp_space_sigopt(lowercase   )





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''wandb'''
				@staticmethod
				def     lowerCAmelCase_  (		):



						"""simple docstring"""





						return is_wandb_available()
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						return run_hp_search_wandb(lowercase						,      lowercase						,      lowercase						,      **lowercase   )







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						return default_hp_space_wandb(lowercase   )

_UpperCAmelCase          =						{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}




def 							UpperCamelCase		(			):
		'''simple docstring'''




		A_						:							List[str]      = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
		if len(__lowercase      ) > 0:
				A_						:							List[str]      = available_backends[0].name
				if len(__lowercase      ) > 1:
						logger.info(
						    f'''{len(__lowercase      )} hyperparameter search backends available. Using {name} as the default.'''      )
				return name
		raise RuntimeError(
		    'No hyperparameter search backend available.\n'
		    + '\n'.join(
		        f''' - To install {backend.name} run {backend.pip_install()}'''
		        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()      )      )






